Generally, flow line detection of a mobile body such as a person or an object is realized by associating a position of the mobile body (trajectory) and an ID (identification information) of the mobile body. This association makes it possible to uniquely identify the mobile body and detect a flow line.
Further, various techniques related to distribution processing are proposed (see, for example, Patent Literatures 1 and 2).
Patent Literature 1 discloses a load distribution method targeting at a capture apparatus which performs a plurality of types of analysis processing of data sent in a network. The load distribution method disclosed in Patent Literature 1 includes: an actual load measurement step of measuring an actual load amount by analysis processing per capture apparatus; a load amount update step of updating information of the actual load amount per measurement, and updating information of a predicted load amount of analysis processing to be executed next, based on the information of the actual load amount; a required time measurement step of measuring a required time required for each analysis processing; a processing frequency calculation step of calculating a processing frequency which indicates a degree of each analysis processing in a time slot for each analysis processing; a target load amount calculation step of calculating a target load amount of each analysis processing allocated to one capture apparatus based on the information of the required time and the information of the processing frequency; and an analysis processing allocation step of, when the actual load amount in this one capture apparatus is higher than a predicted load amount in another capture apparatus, allocating part of analysis processing to be allocated to this one capture apparatus as analysis processing of another capture apparatus, based on the information of the predicted load amount in another capture apparatus and information of the calculated target load amount.
Thus, the load distribution method disclosed in Patent Literature 1 distributes a load by predicting a load amount of analysis processing to be performed next, based on a current load amount in each capture apparatus and, when an actual load amount in a given capture apparatus is higher than a predicted load amount of another capture apparatus, allocating part of analysis processing of the capture apparatus of the higher actual load amount to another capture apparatus of the lower predicted load amount. That is, the analysis processing is distributed using the actual load amount and the predicted load amount as indices.
Further, Patent Literature 2 discloses a parallel data analysis apparatus which targets at analyzing data including a plurality of fields (for example, the sex and the age of clients) such as sales content of a retailer and a use history of a credit card. The parallel data analysis apparatus disclosed in Patent Literature 2 has: a field selection means which selects one or more fields which are not analysis targets per processing apparatus from base fields of a plurality of fields to be commonly allocated to each processing apparatus; a field deletion means which deletes data which belongs to the fields selected by the field selection means per processing apparatus from data which belongs to the base fields; a data analysis means which analyzes data which belongs to the base fields and the deleted data per processing apparatus, and creates a prediction model; a data prediction means which predicts data of a prediction target field based on the prediction model; and a prediction model selection means which compares prediction results and uses a prediction model of the highest analysis precision as a prediction value.
Thus, the parallel data analysis apparatus disclosed in Patent Literature 2 excludes one or more fields from a plurality of fields to be allocated per parallelized processing apparatus, analyzes the field using the excluded data and selects an analysis result of the highest precision. That is, the parallel data analysis apparatus uses a field which configures data to distribute input information to each processing apparatus. Further, although data analysis procedure includes supervised learning and unsupervised learning, in case of supervised learning, the degree of data association between fields is calculated, so that field data of a high degree of association can be processed by an identical processing apparatus.